CIA Team Training

1. CIA Team Training Presented on September 2nd and 3rd, 2014 by Susan Compton, Ed. D, Rosemary Schutz, and Shawn Feeney.

2. CIA Teams • If we believe all kids can learn… • What is it we expect them to learn? • How will we know when they have learned it? • How will we respond when they don’t learn? • How will we respond when they already know it?

3. CIA Teams • C.I.A.: • Curriculum, Instruction, Assessment. • Objective: • In order to maximize student achievement in our schools, CIA Teams will learn about and begin utilizing the Data Action Model.

4. CIA Teams • Goal #1: • CIA Teams will learn the Data Action Model. • Goal #2: • CIA Teams will engage in Data Meeting One, using the “Ask and Wonder” protocol. Subsequently, CIA Teams will prepare for Data Meeting Two, scheduled for October 10th, for which they will bring additional assessment information in order to “triangulate” data to draw conclusions.

5. CIA Teams • Goal #3: • After engaging in today’s Data Meeting #1, teachers will preview and consider connections that can be made between their CIA Team’s data analysis and their individual SGOs.

6. CIA Teams • Before we talk about CIA teams further, please take a look at this video clip and consider two questions: • What is the purpose of this team’s work? • How might the team’s work improve student learning?

7. CIA Teams • Please talk to a neighbor about the questions we considered while watching the clip: • What is the purpose of this team’s work? • How might the team’s work improve student learning?

8. CIA Teams • On a scale of 1-10, with a “1” representing “Not at all” and a “10” representing “Often, routinely, and very well,” answer this question: • “How often or effectively do I use data to inform my classroom instruction?” Think for a minute, and then tell someone nearby you about your experiences with data in the classroom.

9. CIA Teams • How many of you gave yourself a score of 1-5? • How many gave it a 6-10?

10. CIA Teams • It is important to think about CIA teams in broad terms, even though we will use specific protocols identified in the Data Action Model. Take a look at this next clip and consider which aspect of CIA – curriculum, instruction, or assessment – is emphasized most in these CIA teams…

11. CIA Teams • Tell your neighbor about which aspect you would say was emphasized most by the team in the video: curriculum, instruction, or assessment?

12. CIA Teams • Can you see the potential for student learning improvement as a result of this kind of collaboration?

13. CIA Teams • While we are proud of the academic gains we have made in our schools in recent years, we know we still have much room for improvement. There is a common mistake school districts make when it comes to teaching and learning and data. And that is that we often rely on hunches.

14. CIA Teams • I want to talk to you for a minute about a simple protocol we will use in the Tri-District this year, called the Data Action Model. This model helps us drill down deeper into the data, so that we don’t “misdiagnose” issues, which wastes time and does not increase student achievement.

15. REFERENCING TEXT How Teachers Can Turn Data Into Action (ASCD, 2014) #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

16. CIA Teams • The Data Action Model helps us avoid operating merely on hunches, which can often be incorrect, causing us to waste our effort and time.

17. CIA Teams • Please take a look at page 11 of your text How Teachers Can Turn Data Into Action. This passage illustrates the usefulness of the Data Action Model that our CIA Teams can use.

18. CIA Teams • Let’s review the Data Action Model to see its benefits.

19. The DATA ACTION MODEL Schools tend to be: • Good at amassing data • Good at looking (or attempting to look) at data • Bad at responding to the data • Bad at changing instructional practice based on the data #data2action Need for a Process 69th ANNUAL CONFERENCE & EXHIBIT SHOW

20. The DATA ACTION MODEL Systematic, User-friendly • Slows the process down • Avoids rushing to conclusions • Relies on evidence rather than “hunches” • Forces instructional change #data2action Data Action Model 69th ANNUAL CONFERENCE & EXHIBIT SHOW

21. The DATA ACTION MODEL Organized by Meetings • Review Existing Data • Explore additional, relevant data • Identify Learning Gaps • Link to Instructional Gaps • Set a goal and Action Plan (that includes new instructional strategies) • Implement in Classroom • Evaluate Success At a Glance (the Nutshellized version) #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

22. The DATA ACTION MODEL DATA MEETING 1 Reviewing Existing Data & Asking Questions DATA MEETING 2 Triangulating the Data DATA MEETING 3 Determining Gaps & Goals DATA MEETING 4 Planning for Action DATA MEETING 5 Evaluating Success & Next Steps #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

23. CIA Teams • Let’s also take a look at a chart that depicts the Data Action Model on page 4 of your text.

24. CIA Teams

25. CIA Teams • Today, we are going to review the process for Data Meeting #1 (DM1) and then conduct our first data meeting.

26. CIA Teams • Before we do that, let’s take a closer look at what needs to happen in DM1. • Please refer to pp. 21-36 for a detailed description of DM1 • Please see page 114 for the “Notice and Wonder Protocol.”

27. The DATA ACTION MODEL Reviewing Existing Data and Asking Questions 1. Notice & Wonder Protocol 2. Ask Exploratory Questions Decide which artifacts are needed & who will bring them #data2action Data Meeting 1 69th ANNUAL CONFERENCE & EXHIBIT SHOW

28. The DATA ACTION MODEL (Sample Data for Discussion Purposes) #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

29. The DATA ACTION MODEL Notice Statements • factual observations • free of inference and speculation • no mention of causality • reflect what is present (in the data) and not why this may be so #data2action Notice & Wonder 69th ANNUAL CONFERENCE & EXHIBIT SHOW

30. The DATA ACTION MODEL (Sample Data for Discussion Purposes) #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

31. The DATA ACTION MODEL Wonder Statements • “I wonder {why, if, how, whether}” • may be speculative, inferential • may reflect possible causes • may be comparative • should include statements about instruction (and not merely about student performance) • may or may not be linked to Notice Statements Notice & Wonder #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

32. The DATA ACTION MODEL (Sample Data for Discussion Purposes) #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

33. The DATA ACTION MODEL Exploratory Questions • are derived from Wonders • reflect past practice or results • provide the compass for gathering artifacts/more info • focus the team (and filter out extraneous information) • address issues worthy of investigation • lead to identifying gaps Exploratory Questions #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

34. CIA Teams • The rest of the Data Action Model calls for these steps…

35. The DATA ACTION MODEL Triangulating the Data • Review of artifacts • Focus Exploratory Question • Track Learning & Instructional Gaps #data2action Data Meeting 2 69th ANNUAL CONFERENCE & EXHIBIT SHOW

36. The DATA ACTION MODEL #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

37. The DATA ACTION MODEL Determining Gaps & Goals 1. Identify Learning gaps 2. Identify Instructional gaps Set a Target Learning Goal Decide an evaluation metric #data2action Data Meeting 3 69th ANNUAL CONFERENCE & EXHIBIT SHOW

38. The DATA ACTION MODEL Strategies Search • Einstein’s Insanity • Something old, new, borrowed, blue #data2action Between DM3 and DM4 69th ANNUAL CONFERENCE & EXHIBIT SHOW

39. The DATA ACTION MODEL #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

40. The DATA ACTION MODEL Planning for Action 1. Review new strategies/activities Develop a Data Action Plan #data2action Data Meeting 4 69th ANNUAL CONFERENCE & EXHIBIT SHOW

41. The DATA ACTION MODEL #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

42. The DATA ACTION MODEL Implementation (where the “rubber meets the road”) • 2 – 4 weeks • PLC continues to meet during Implementation Period • Looks at Teacher Work, Student Work, Issues & Dilemmas, Text #data2action Between DM4 and DM5 69th ANNUAL CONFERENCE & EXHIBIT SHOW

43. The DATA ACTION MODEL Data Meeting 5 Evaluate Success • Use Evaluation Metric • Decide next steps #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

44. The DATA ACTION MODEL Questions? #data2action 69th ANNUAL CONFERENCE & EXHIBIT SHOW

45. CIA Teams • In just a moment, we are going to move into our teams to begin our first Data Meeting. You will notice the Chromebooks we have set up on the tables for your teams to borrow. When you sit together with your team, you will be asked to document your discussion today, and we will use Google Drive as a place to include data and notes relevant to our CIA teamwork together.

46. CIA Teams • Before conducting your first meeting, you will need to identify roles first. You will need the following roles: • Facilitator • Timekeeper • Recorder • Active Participants

47. CIA Teams • Similarly, it will be helpful to review the “Notice and Wonder” protocol on page 114 before beginning. • It may also be helpful to review the description of DM1 on pp. 21-36 before delving into the data.

48. CIA Teams • In a “nutshell” you will accomplish three things in your DM1 today: • Do First: Review Existing Data • Do Second: Ask Exploratory Questions • Do Third: Decide Who Will Bring What to DM2 on October 10th.

49. CIA Teams • A word about data: • Today’s data is “Macrodata” that can be used as an excellent starting point for us. While this is broad, general data about our student achievement, it is an excellent place to conduct “Notice,” “Wonder,” and “Ask” activities. • Our next meeting (DM2, on October 10th) will allow us to bring additional, more specific data from our individual classrooms so that we can “triangulate” and begin to notice more specific, accurate trends regarding our students’ skills and abilities.

50. CIA Teams • By the end of today’s DM1 meeting, you will have recorded the following items on your DM1 Summary: • Your team’s “Notice Statements” (see page 28) • Your team’s “Wonder Statements” (see page 29) • Your team’s “Exploratory Questions” (see page 31) • Finally, after asking the “Exploratory Questions,” identify who will bring what kind of data to the next meeting; all participants will need to bring something useful. Please see page 33 for samples of relevant data.